Apparatus and method for selecting a bandwidth  prediction source

ABSTRACT

Aspects of the subject disclosure may include, for example, obtaining, from a first source of information, a first bandwidth prediction, wherein the first bandwidth prediction is based upon historical bandwidth data that had been provided by a plurality of devices; obtaining, from a second source of information, a second bandwidth prediction, wherein the second bandwidth prediction is based upon network measurements, and wherein the network measurements are other than the historical bandwidth data that had been provided by the plurality of devices; selecting as a source of a future bandwidth prediction one of the first source of information and the second source of information, wherein the selecting is based upon a comparison of each of the first bandwidth prediction and the second bandwidth prediction to an actually obtained bandwidth of the device. Other embodiments are disclosed.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/213,624 filed on Dec. 7, 2018. All sections of the aforementionedapplication are incorporated herein by reference in its entirety.

FIELD OF THE DISCLOSURE

The subject disclosure relates to an apparatus and method for selectinga bandwidth prediction source. In one example, the selecting cancomprise selecting the best bandwidth prediction source for a videoclient at a given time.

BACKGROUND

Predictive video adaptation is an emerging paradigm following thetremendous growth of Over The Top (OTT) video streaming. Video iscurrently the most prominent traffic type, and ever growing both due tonew technologies (e.g., 360 video, augmented reality (AR), virtualreality (VR)) and new video platforms (e.g., PERISCOPE and TWITCH).Improving user experience when watching videos on-line is important bothfor content providers and Internet Service Providers. The conventionalstate-of-the-art for on-line video streaming typically adapts the videoquality based on network conditions. Many different algorithms exist,but they generally monitor the achieved local throughput to decide onthe quality of future chunks (that is, future portions of a video) oruse network-based prediction according to historical throughput data todecide on the quality of future chunks.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference will now be made to the accompanying drawings, which are notnecessarily drawn to scale, and wherein:

FIG. 1 is a block diagram illustrating an example, non-limitingembodiment of a communication network in accordance with various aspectsdescribed herein.

FIG. 2A is a block diagram illustrating an example, non-limitingembodiment of a system 2000 (which can function, for example, fully orpartially within the communication network 100 of FIG. 1) in accordancewith various aspects described herein.

FIG. 2B depicts an illustrative embodiment of a method in accordancewith various aspects described herein.

FIG. 2C depicts an illustrative embodiment of a method in accordancewith various aspects described herein.

FIG. 2D depicts an illustrative embodiment of a method in accordancewith various aspects described herein.

FIG. 2E is a block diagram illustrating an example, non-limitingembodiment of a system 2400 (which can function, for example, fully orpartially within the communication network 100 of FIG. 1) in accordancewith various aspects described herein.

FIG. 2F is a block diagram illustrating an example, non-limitingembodiment of a system 2500 (which can function, for example, fully orpartially within the communication network 100 of FIG. 1) in accordancewith various aspects described herein.

FIG. 3 is a block diagram illustrating an example, non-limitingembodiment of a virtualized communication network in accordance withvarious aspects described herein.

FIG. 4 is a block diagram of an example, non-limiting embodiment of acomputing environment in accordance with various aspects describedherein.

FIG. 5 is a block diagram of an example, non-limiting embodiment of amobile network platform in accordance with various aspects describedherein.

FIG. 6 is a block diagram of an example, non-limiting embodiment of acommunication device in accordance with various aspects describedherein.

DETAILED DESCRIPTION

The subject disclosure describes, among other things, illustrativeembodiments for selecting a bandwidth prediction source. In one example,the selecting can comprise selecting the best bandwidth predictionsource for a video client at a given time. Other embodiments aredescribed in the subject disclosure.

In one embodiment, multiple sources of prediction can be utilized: (i)local historical data (e.g., local historical throughput data); (ii)network historical data (e.g., network-wide historical throughput datathat is reported by and/or obtained from a plurality of devices(essentially a large pool of “local historical throughput data”));and/or (iii) network measurement-based data (e.g., from internal networkmetrics). In one specific example, each of local predictions (based, forexample, upon the above-mentioned local historical throughput data),network historical predictions (based, for example, upon theabove-mentioned network historical throughput data), and networkmeasurement-based predictions (based, for example, upon theabove-mentioned internal network metrics) can be validated against adetermination of actual activity (e.g., locally obtained throughput).The determination of actual activity can be obtained, for example, overthe period predicted by any of the sources. Then, it can be decidedwhich one (or more) of the sources is best to use for the next decisionor series of decisions. In another specific example, the best bandwidthprediction source at any given time may not be the same throughout thesame video session or across video sessions. In another specificexample, there is no need to make a commitment towards a bandwidthprediction source ahead of time—simply use some or all of the availablebandwidth prediction source(s) in real-time. In another specificexample, one bandwidth prediction source can be used as a primarysource, another bandwidth prediction source can be used as a secondarysource, yet another bandwidth prediction source can be used as atertiary source, etc. In another specific example, a process to tune orrefine the decision that the primary source suggests can be carried out.

Referring now to FIG. 1, a block diagram is shown illustrating anexample, non-limiting embodiment of a communication network 100 inaccordance with various aspects described herein. In particular, acommunications network 125 is presented for providing broadband access110 to a plurality of data terminals 114 via access terminal 112,wireless access 120 to a plurality of mobile devices 124 and vehicle 126via base station or access point 122, voice access 130 to a plurality oftelephony devices 134, via switching device 132 and/or media access 140to a plurality of audio/video display devices 144 via media terminal142. In addition, communications network 125 is coupled to one or morecontent sources 175 of audio, video, graphics, text and/or other media.While broadband access 110, wireless access 120, voice access 130 andmedia access 140 are shown separately, one or more of these forms ofaccess can be combined to provide multiple access services to a singleclient device (e.g., mobile devices 124 can receive media content viamedia terminal 142, data terminal 114 can be provided voice access viaswitching device 132, and so on).

The communications network 125 includes a plurality of network elements(NE) 150, 152, 154, 156, etc. for facilitating the broadband access 110,wireless access 120, voice access 130, media access 140 and/or thedistribution of content from content sources 175. The communicationsnetwork 125 can include a circuit switched or packet switched network, avoice over Internet protocol (VoIP) network, Internet protocol (IP)network, a cable network, a passive or active optical network, a 4G, 5G,or higher generation wireless access network, WIMAX network,UltraWideband network, personal area network or other wireless accessnetwork, a broadcast satellite network and/or other communicationsnetwork.

In various embodiments, the access terminal 112 can include a digitalsubscriber line access multiplexer (DSLAM), cable modem terminationsystem (CMTS), optical line terminal (OLT) and/or other access terminal.The data terminals 114 can include personal computers, laptop computers,netbook computers, tablets or other computing devices along with digitalsubscriber line (DSL) modems, data over coax service interfacespecification (DOCSIS) modems or other cable modems, a wireless modemsuch as a 4G, 5G, or higher generation modem, an optical modem and/orother access devices.

In various embodiments, the base station or access point 122 can includea 4G, 5G, or higher generation base station, an access point thatoperates via an 802.11 standard such as 802.11n, 802.11ac or otherwireless access terminal. The mobile devices 124 can include mobilephones, e-readers, tablets, phablets, wireless modems, and/or othermobile computing devices.

In various embodiments, the switching device 132 can include a privatebranch exchange or central office switch, a media services gateway, VoIPgateway or other gateway device and/or other switching device. Thetelephony devices 134 can include traditional telephones (with orwithout a terminal adapter), VoIP telephones and/or other telephonydevices.

In various embodiments, the media terminal 142 can include a cablehead-end or other TV head-end, a satellite receiver, gateway or othermedia terminal 142. The display devices 144 can include televisions withor without a set top box, personal computers and/or other displaydevices.

In various embodiments, the content sources 175 include broadcasttelevision and radio sources, video on demand platforms and streamingvideo and audio services platforms, one or more content data networks,data servers, web servers and other content servers, and/or othersources of media.

In various embodiments, the communications network 125 can includewired, optical and/or wireless links and the network elements 150, 152,154, 156, etc. can include service switching points, signal transferpoints, service control points, network gateways, media distributionhubs, servers, firewalls, routers, edge devices, switches and othernetwork nodes for routing and controlling communications traffic overwired, optical and wireless links as part of the Internet and otherpublic networks as well as one or more private networks, for managingsubscriber access, for billing and network management and for supportingother network functions.

Referring now to FIG. 2A, this is a block diagram illustrating anexample, non-limiting embodiment of a system 2000 (all or part of whichcan function within the communication network 100 of FIG. 1) inaccordance with various aspects described herein. As seen in this FIG.2A, an application 2001 (which can run, for example, on a video player)can make a local prediction (e.g., based on local historical informationaccessible by the application/video player). The local prediction (whichcan be a future bandwidth prediction) by the application/video playercan be reported to a network mechanism 2002 in the cloud (see arrow“A”). In addition, the application/video player can report to thenetwork mechanism 2002 in the cloud various states (e.g., buffer level)of the application/video player as well as various decisions (e.g.,video quality level requests) that have been made by theapplication/video player (see, again, arrow “A”). The mechanism 2002 inthe cloud can provide to the application/video player throughputguidance (see arrow B). This throughput guidance can be guidance basedupon learning (e.g., machine learning). This throughput guidance can bebased upon historical data from many devices (e.g., various devices thatcommunicate with a given network). This throughput guidance can providepredictions and/or can tell the application/player what to do (e.g.,what video quality to request for a subsequent video segment). Inaddition, mechanism 2003 in the cloud can provide to theapplication/video player network guidance (see arrow C). The networkguidance can comprise and/or be based upon network measurements (e.g.,signal to noise ratio, signal quality, radio resource utilization). Thenetwork guidance can inform the application/video player of networkconditions (and/or can provide one or more throughput predictions basedupon such network measurements). The application logic (of theapplication/video player) can receive the inputs B and C (as well aslocal historical-based predictions shown at arrow D) and can make one ormore decisions as to which type of prediction (e.g., local historicalthroughput, network historical throughput and/or networkmeasurement-based) and/or which prediction source (e.g., 2001, 2002,and/or 2003) to utilize. In one example, the information sent at arrow“D” can have been summarized. In another example, the information sentat arrow “D” can be the result of analysis. Once a decision is made (ordecisions are made) as to which type of prediction to utilize and/orwhich prediction source to utilize, the application/video player candetermine the most appropriate video quality to request from server 2004when the application/video player requests the next segment of video(see the block labeled “Decision Execution”). The application/videoplayer can then receive the video data and present the video to a user.

In one example, the network measurements can be collected continuously,at sub-second or at about 1-second granularity. In another example, thenetwork measurements can be collected for all devices connected (e.g.phones, cars, shipping containers, etc.) In another example, the networkmeasurements can be collected only for those devices for whichthroughput guidance is deemed to be useful. In one specific example, thenetwork measurements can be collected only for consumer devices that canstream video. In another specific example, the network measurements caninstead (or also) be collected for cars (e.g., for firmware updatesand/or in-car entertainment system/WiFi). In another example, thenetwork measurements can be collected for one or more specific“Services” (e.g., when the network operator (e.g., provider of guidance)and service provider (e.g., whose app/service uses the guidance) have an“agreement” that such an app/service will use the guidance). In onespecific example, wherein the network measurements are collected for oneor more specific “Services”, a mechanism can be set up where themeasurements relevant to only the devices running this app/service wouldbe collected. In one example, the network measurements can be collectedin some parts of the network. In another example, the networkmeasurements can be collected in all parts of the network. In anotherexample, all radio cells would be subject to network measurements.

Referring now to FIG. 2B, various steps of a method 2100 according to anembodiment are shown. As seen in this FIG. 2B, step 2102 comprisesobtaining, from a first source of information, a first bandwidthprediction, wherein the first bandwidth prediction is based uponhistorical bandwidth data that had been provided from a plurality ofdevices. In one example, the historical bandwidth data can be providedto a network from the plurality of devices. In another example, thefirst bandwidth prediction can be based upon, for example, individualbandwidth that had been provided to various devices and/or based upon anaggregated bandwidth that had been provided to the various devices.Next, step 2104 comprises obtaining, from a second source ofinformation, a second bandwidth prediction, wherein the second bandwidthprediction is based upon network measurements, and wherein the networkmeasurements are other than the historical bandwidth data that had beenprovided from the plurality of devices (the network measurements can bebased upon, for example, communications with individual devices and/orbased upon aggregated communication with multiple devices). Next, step2106 comprises selecting as a source of a future bandwidth predictionone of the first source of information and the second source ofinformation, wherein the selecting is based upon a comparison of each ofthe first bandwidth prediction and the second bandwidth prediction to anactually obtained bandwidth of the device.

While for purposes of simplicity of explanation, the respectiveprocesses are shown and described as a series of blocks in FIG. 2B, itis to be understood and appreciated that the claimed subject matter isnot limited by the order of the blocks, as some blocks may occur indifferent orders and/or concurrently with other blocks from what isdepicted and described herein. Moreover, not all illustrated blocks maybe required to implement the methods described herein.

Referring now to FIG. 2C, various steps of a method 2200 according to anembodiment are shown. As seen in this FIG. 2C, step 2202 comprisesreceiving, from a network, a first bandwidth prediction that is basedupon historical bandwidth data that had been provided to the network bya plurality of devices. Next, step 2204 comprises receiving, from thenetwork, a second bandwidth prediction that is based upon one or moremeasurements of the network, wherein one or more measurements of thenetwork are other than the historical bandwidth data that had beenprovided to the network by the plurality of devices. Next, step 2206comprises receiving, from a video server via the network, a first videosegment. Next, step 2208 comprises determining a bandwidth that hadactually been obtained by the video player during receipt of the firstvideo segment. Next, step 2210 comprises selecting, as a selected sourceof information, a first source that had facilitated providing the firstbandwidth prediction or a second source that had facilitated providingthe second bandwidth prediction, wherein the first source is selected ina first case that the first bandwidth prediction is closer to thebandwidth that had actually been obtained than the second bandwidthprediction, and wherein the second source is selected in a second casethat the second bandwidth prediction is closer to the bandwidth that hadactually been obtained than the first bandwidth prediction. Next, step2212 comprises obtaining, from the selected source of information, aprediction of a future available bandwidth. Next, step 2214 comprisesdetermining a video quality level to request based upon the predictionof the future available bandwidth. Next, step 2216 comprises requesting,from the video server, a second video segment having the video qualitylevel that had been determined.

While for purposes of simplicity of explanation, the respectiveprocesses are shown and described as a series of blocks in FIG. 2C, itis to be understood and appreciated that the claimed subject matter isnot limited by the order of the blocks, as some blocks may occur indifferent orders and/or concurrently with other blocks from what isdepicted and described herein. Moreover, not all illustrated blocks maybe required to implement the methods described herein.

Referring now to FIG. 2D, various steps of a method 2300 according to anembodiment are shown. As seen in this FIG. 2D, step 2302 comprisesreceiving, by a video player comprising a processing system with aprocessor, a first bandwidth prediction, wherein the first bandwidthprediction is based upon historical bandwidth data that had beenprovided to a network by a plurality of devices, wherein the historicalbandwidth data incudes first data indicative of a first historicalbandwidth that had been achieved by the video player, and wherein thehistorical bandwidth data incudes second data indicative of a pluralityof second historical bandwidths that had been achieved by other devicesof the plurality of devices. Next, step 2304 comprises receiving, by thevideo player, a second bandwidth prediction, wherein the secondbandwidth prediction is based upon one or more measurements of thenetwork, and wherein the one or more measurements of the network areother than the historical bandwidth data that had been provided to thenetwork by the plurality of devices. Next, step 2305 comprisesdetermining, by the video player, a third bandwidth prediction, whereinthe third bandwidth prediction is based upon the first data indicativeof the first historical bandwidth that had been achieved by the videoplayer. Next, step 2306 comprises playing, by the video player, a firstportion of a video, wherein the first portion of the video is obtainedby the video player via the network from a video source. Next, step 2308comprises calculating, by the video player, a bandwidth that hadactually been obtained by the video player during receipt of the firstportion of the video. Next, step 2310 comprises selecting, by the videoplayer, as a selected source of information, a first portion of thenetwork that had provided the first bandwidth prediction, a secondportion of the network that had provided the second bandwidthprediction, or the video player as a local source of prediction, whereinthe first portion of the network is selected in a first case that thefirst bandwidth prediction is closer to the bandwidth that had beenactually obtained than both the second bandwidth prediction and thethird bandwidth prediction, wherein the second portion is selected in asecond case that the second bandwidth prediction is closer to thebandwidth that had been actually obtained than both the first bandwidthprediction and the third bandwidth prediction, and wherein the videoplayer as the local source of prediction is selected in a third casethat the third bandwidth prediction is closer to the bandwidth that hadbeen actually obtained than both the first bandwidth prediction and thesecond bandwidth prediction. Next, step 2312 comprises obtaining, by thevideo player, from the selected source of information, a prediction of afuture available bandwidth. Next, step 2314 comprises requesting, by thevideo player, from the video source, a second portion of the videohaving a selected bitrate, wherein the selected bitrate is chosen by thevideo player based upon the prediction of the future availablebandwidth. In one example, the video player can then play the video asthe video is received at the selected bitrate.

While for purposes of simplicity of explanation, the respectiveprocesses are shown and described as a series of blocks in FIG. 2D, itis to be understood and appreciated that the claimed subject matter isnot limited by the order of the blocks, as some blocks may occur indifferent orders and/or concurrently with other blocks from what isdepicted and described herein. Moreover, not all illustrated blocks maybe required to implement the methods described herein.

Referring now to FIG. 2E, it is seen that system 2400 can include VideoPlayer 2402, Server “A” 2404, and Server “B” 2406. This system 2400 canalso include Other Device A 2408, Other Device B 2410, Other Device C2412 and Other Device D 2414 (each of which can be a video player and/oranother type of network-connectable device). In this embodiment, Server“A” 2404 acts as a first source of prediction information to the VideoPlayer 2402 and/or provides a first type of prediction information tothe Video Player 2402. Further, Server “B” 2406 acts as a second sourceof prediction information to the Video Player 2402 and/or provides asecond type of prediction information to the Video Player 2402. In oneexample, the Video Player 2402 is in bi-directional communication witheach of Server “A” 2404 and Server “B” 2406. In another example, each ofOther Device A 2408 and Other Device B 2410 is in bi-directionalcommunication with Server A 2404. In another example, each of OtherDevice C 2412 and Other Device D 2414 is in bi-directional communicationwith Server B 2406. In another example, the bi-directional communicationis via a network (for instance, the Internet).

Referring now to FIG. 2F, it is seen that system 2500 can include VideoPlayer 2502 and Server 2504. This system 2500 can also include OtherDevice A 2508, Other Device B 2510, Other Device C 2512 and Other DeviceD 2514 (each of which can be a video player and/or another type ofnetwork-connectable device). In this embodiment, Server 2504: (a) actsas both a first source of prediction information to the Video Player2502 and as a second source of prediction information to the VideoPlayer 2502; and/or (b) provides both a first type of predictioninformation to the Video Player 2502 and a second type of predictioninformation to the Video Player 2502. In one example, the Video Player2502 is in bi-directional communication with Server 2504. In anotherexample, each of Other Device A 2508, Other Device B 2510, Other DeviceC 2512 and Other Device D 2514 is in bi-directional communication withServer 2504. In another example, the bi-directional communication is viaa network (for instance, the Internet).

As described herein, a number of techniques can be utilized to predictbandwidth (e.g., network bandwidth) that will be available for a device(e.g., a client device such as a video player): locally-based prediction(e.g., local historical throughput); and network-based prediction (e.g.,historical throughput and/or metric measurements). Historical throughputcan be determined based upon previous communications (e.g., videochunks). Such historical throughput can be used to derive a bandwidthprediction (e.g., considering the moving average of the historicalthroughput computed over the last N chunks). Network-based prediction(which, for example, can be received from an “oracle” or other remotesource) can result in accurate bandwidth prediction using severalstrategies: (1) perform a similar task as the player; (2) use historicalthroughput (e.g. from many players, wherein the information related tothe many players can be combined into a source of good predictionsleading to good decisions in terms of selected future quality); and/or(3) analysis of network metrics, such as radio conditions in a cellularnetwork (wherein such analysis can be combined into a source of goodpredictions leading to good decisions in terms of selected futurequality). One or more of, for example, the network-based approaches canuse a machine learning (ML) approach.

Various embodiments can provide for a player having access to both localand network-based information and deciding which one to “believe” orprioritize and ultimately use. In one example, local historicalthroughput can be used as a validation of network throughput. In oneexample, local historical throughput can be used to continuouslyevaluate the quality of the network data. In one specific example, athreshold-based system facilitates selecting of the best data source,and/or facilitates switching (possibly back and forth) between two ormore data sources (e.g., when the network-based predictions have anaccuracy higher than 90%, the player will prefer these predictions). Inanother example, local historical throughput can be used as a fallbackmechanism in the absence of network data or in the presence ofinaccurate predictions from network data.

In various embodiments, by bringing all prediction possibilities intothe video player an opportunity is provided to make better decisions andto improve customer experience. In the face of a multitude of devicesand network conditions that such devices can experience, this canimprove robustness and adaptability. For example, an ANDROIDimplementation of the DIRECTV app (player) can be installed on acellphone, tablet or an ANDROID TV box. Cellphones can be connected todifferent cellular operators, tablets experience various WiFi conditionsand sometimes cellular, while TV boxes could operate on a high-speedbackhaul or slow DSL. All of such devices could eventually end upstreaming over a 5G cell. It can be impossible to know which bandwidthprediction source is the best for each device and/or condition at agiven time. Therefore, according to various embodiments, havingbandwidth prediction source selection implemented dynamically canprovide the best solution.

Improved customer experience can be critical to reduce churn and gainnew customers for a provider. Various techniques described herein canimprove the overall service.

In another embodiment, the overall decision making process does not haveto be limited to bandwidth prediction (it could be congestion and/orsome other network state or characteristic).

In another embodiment, the implementation does not have to be limited tovideo players (the implementation can be applied to any application thatcan utilize a similar decision-making process).

As described herein, mechanisms are provided to leverage the best ofboth worlds—local historical throughput and network-based predictions.This combination can be valuable, as it can: (a) provide for judiciouslyselecting the best option; and (b) be robust to inaccurate predictions.In addition, this combination can be highly aligned with variousnetwork-predictive solutions emerging at this time.

As described herein, mechanisms can select one or more bandwidthprediction source(s) that provide: (a) local historical throughput data;(b) historical-based predictions from the network (e.g., based onhistorical data associated with many devices); and/or (c) real-time(instantaneous and/or near-instantaneous) predictions from the network(e.g., based on network measurements).

In one example, a selection mechanism (such as an application) can runon a tablet, a cellphone, a smartphone, or a set-top box.

In one example, a single prediction source can be used by theapplication/video player to make the determination as to the videoquality of the next video segment. In another example, a plurality ofthe prediction sources (e.g., utilizing a prioritization and/orweighting) can be used by the application/video player to make thedetermination as to the video quality of the next video segment.

In one example, the application does not need to be associated with avideo player but, instead, could be any application that changesbehavior based upon future throughput (and/or changes behavior basedupon any other future characteristic(s)).

As described herein various mechanisms provide video player vendorscertain benefits, especially as the opportunities for cloud-basedadaptation and throughput prediction increase in the future.

As described herein, by providing certain throughput guidance based uponhistorical data of many devices, better decisions can be achieved.

As described herein, various embodiments provide for selecting the bestprediction source. The selection can be performed, for example, usingcomparisons and/or machine learning. In one specific example, thecomparisons can be based upon a “true” historical throughput value.

In one specific example, a video player can compare an actual throughputvalue to a value that had been predicted by a network source (e.g.,based on throughput) to device whether the network source issufficiently accurate to use in the future.

In another specific example, a video player can compare an actualthroughput value to a value that had been predicted by a network source(e.g., based on network measurements) to device whether the networksource is sufficiently accurate to use in the future.

In another specific example, a video player can compare a locallydetermined throughput value to a value that had been predicted by anetwork source (e.g., based on network measurements and/or based onnetwork measurements) to device whether the local determination issufficiently accurate to use in the future.

In another specific example, various determinations described herein canbe performed in an iterative process.

In another specific example, a mechanism can arbitrate between a localhistorically-based prediction and one or more network-based predictions.

In another example, predictions from various sources can be tracked,analyzed, compared and/or utilized in one or more decision makingprocesses.

In another example, network throughput prediction(s) can be based upondata associated with the device receiving the prediction(s).

Referring now to FIG. 3, a block diagram 300 is shown illustrating anexample, non-limiting embodiment of a virtualized communication networkin accordance with various aspects described herein. In particular avirtualized communication network is presented that can be used toimplement some or all of the subsystems and functions of communicationnetwork 100, the subsystems and functions of system 2000, and methods2100, 2200 and 2300 presented in FIGS. 1, 2A, 2B, 2C.

In particular, a cloud networking architecture is shown that leveragescloud technologies and supports rapid innovation and scalability via atransport layer 350, a virtualized network function cloud 325 and/or oneor more cloud computing environments 375. In various embodiments, thiscloud networking architecture is an open architecture that leveragesapplication programming interfaces (APIs); reduces complexity fromservices and operations; supports more nimble business models; andrapidly and seamlessly scales to meet evolving customer requirementsincluding traffic growth, diversity of traffic types, and diversity ofperformance and reliability expectations.

In contrast to traditional network elements—which are typicallyintegrated to perform a single function, the virtualized communicationnetwork employs virtual network elements 330, 332, 334, etc. thatperform some or all of the functions of network elements 150, 152, 154,156, etc. For example, the network architecture can provide a substrateof networking capability, often called Network Function VirtualizationInfrastructure (NFVI) or simply infrastructure that is capable of beingdirected with software and Software Defined Networking (SDN) protocolsto perform a broad variety of network functions and services. Thisinfrastructure can include several types of substrates. The most typicaltype of substrate being servers that support Network FunctionVirtualization (NFV), followed by packet forwarding capabilities basedon generic computing resources, with specialized network technologiesbrought to bear when general purpose processors or general purposeintegrated circuit devices offered by merchants (referred to herein asmerchant silicon) are not appropriate. In this case, communicationservices can be implemented as cloud-centric workloads.

As an example, a traditional network element 150 (shown in FIG. 1), suchas an edge router can be implemented via a virtual network element 330composed of NFV software modules, merchant silicon, and associatedcontrollers. The software can be written so that increasing workloadconsumes incremental resources from a common resource pool, and moreoverso that it's elastic: so the resources are only consumed when needed. Ina similar fashion, other network elements such as other routers,switches, edge caches, and middle-boxes are instantiated from the commonresource pool. Such sharing of infrastructure across a broad set of usesmakes planning and growing infrastructure easier to manage.

In an embodiment, the transport layer 350 includes fiber, cable, wiredand/or wireless transport elements, network elements and interfaces toprovide broadband access 110, wireless access 120, voice access 130,media access 140 and/or access to content sources 175 for distributionof content to any or all of the access technologies. In particular, insome cases a network element needs to be positioned at a specific place,and this allows for less sharing of common infrastructure. Other times,the network elements have specific physical layer adapters that cannotbe abstracted or virtualized, and might require special DSP code andanalog front-ends (AFEs) that do not lend themselves to implementationas virtual network elements 330, 332 or 334. These network elements canbe included in transport layer 350.

The virtualized network function cloud 325 interfaces with the transportlayer 350 to provide the virtual network elements 330, 332, 334, etc. toprovide specific NFVs. In particular, the virtualized network functioncloud 325 leverages cloud operations, applications, and architectures tosupport networking workloads. The virtualized network elements 330, 332and 334 can employ network function software that provides either aone-for-one mapping of traditional network element function oralternately some combination of network functions designed for cloudcomputing. For example, virtualized network elements 330, 332 and 334can include route reflectors, domain name system (DNS) servers, anddynamic host configuration protocol (DHCP) servers, system architectureevolution (SAE) and/or mobility management entity (MME) gateways,broadband network gateways, IP edge routers for IP-VPN, Ethernet andother services, load balancers, distributers and other network elements.Because these elements don't typically need to forward large amounts oftraffic, their workload can be distributed across a number ofservers—each of which adds a portion of the capability, and overallwhich creates an elastic function with higher availability than itsformer monolithic version. These virtual network elements 330, 332, 334,etc. can be instantiated and managed using an orchestration approachsimilar to those used in cloud compute services.

The cloud computing environments 375 can interface with the virtualizednetwork function cloud 325 via APIs that expose functional capabilitiesof the VNE 330, 332, 334, etc. to provide the flexible and expandedcapabilities to the virtualized network function cloud 325. Inparticular, network workloads may have applications distributed acrossthe virtualized network function cloud 325 and cloud computingenvironment 375 and in the commercial cloud, or might simply orchestrateworkloads supported entirely in NFV infrastructure from these thirdparty locations.

Turning now to FIG. 4, there is illustrated a block diagram of acomputing environment in accordance with various aspects describedherein. In order to provide additional context for various embodimentsof the embodiments described herein, FIG. 4 and the following discussionare intended to provide a brief, general description of a suitablecomputing environment 400 in which the various embodiments of thesubject disclosure can be implemented. In particular, computingenvironment 400 can be used in the implementation of network elements150, 152, 154, 156, access terminal 112, base station or access point122, switching device 132, media terminal 142, and/or virtual networkelements 330, 332, 334, etc. Each of these devices can be implementedvia computer-executable instructions that can run on one or morecomputers, and/or in combination with other program modules and/or as acombination of hardware and software.

Generally, program modules comprise routines, programs, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Moreover, those skilled in the art will appreciatethat the inventive methods can be practiced with other computer systemconfigurations, comprising single-processor or multiprocessor computersystems, minicomputers, mainframe computers, as well as personalcomputers, hand-held computing devices, microprocessor-based orprogrammable consumer electronics, and the like, each of which can beoperatively coupled to one or more associated devices.

As used herein, a processing circuit includes one or more processors aswell as other application specific circuits such as an applicationspecific integrated circuit, digital logic circuit, state machine,programmable gate array or other circuit that processes input signals ordata and that produces output signals or data in response thereto. Itshould be noted that while any functions and features described hereinin association with the operation of a processor could likewise beperformed by a processing circuit.

The illustrated embodiments of the embodiments herein can be alsopracticed in distributed computing environments where certain tasks areperformed by remote processing devices that are linked through acommunications network. In a distributed computing environment, programmodules can be located in both local and remote memory storage devices.

Computing devices typically comprise a variety of media, which cancomprise computer-readable storage media and/or communications media,which two terms are used herein differently from one another as follows.Computer-readable storage media can be any available storage media thatcan be accessed by the computer and comprises both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media can be implementedin connection with any method or technology for storage of informationsuch as computer-readable instructions, program modules, structured dataor unstructured data.

Computer-readable storage media can comprise, but are not limited to,random access memory (RAM), read only memory (ROM), electricallyerasable programmable read only memory (EEPROM), flash memory or othermemory technology, compact disk read only memory (CD-ROM), digitalversatile disk (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devicesor other tangible and/or non-transitory media which can be used to storedesired information. In this regard, the terms “tangible” or“non-transitory” herein as applied to storage, memory orcomputer-readable media, are to be understood to exclude onlypropagating transitory signals per se as modifiers and do not relinquishrights to all standard storage, memory or computer-readable media thatare not only propagating transitory signals per se.

Computer-readable storage media can be accessed by one or more local orremote computing devices, e.g., via access requests, queries or otherdata retrieval protocols, for a variety of operations with respect tothe information stored by the medium.

Communications media typically embody computer-readable instructions,data structures, program modules or other structured or unstructureddata in a data signal such as a modulated data signal, e.g., a carrierwave or other transport mechanism, and comprises any informationdelivery or transport media. The term “modulated data signal” or signalsrefers to a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in one or moresignals. By way of example, and not limitation, communication mediacomprise wired media, such as a wired network or direct-wiredconnection, and wireless media such as acoustic, RF, infrared and otherwireless media.

With reference again to FIG. 4, the example environment can comprise acomputer 402, the computer 402 comprising a processing unit 404, asystem memory 406 and a system bus 408. The system bus 408 couplessystem components including, but not limited to, the system memory 406to the processing unit 404. The processing unit 404 can be any ofvarious commercially available processors. Dual microprocessors andother multiprocessor architectures can also be employed as theprocessing unit 404.

The system bus 408 can be any of several types of bus structure that canfurther interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures. The system memory 406comprises ROM 410 and RAM 412. A basic input/output system (BIOS) can bestored in a non-volatile memory such as ROM, erasable programmable readonly memory (EPROM), EEPROM, which BIOS contains the basic routines thathelp to transfer information between elements within the computer 402,such as during startup. The RAM 412 can also comprise a high-speed RAMsuch as static RAM for caching data.

The computer 402 further comprises an internal hard disk drive (HDD) 414(e.g., EIDE, SATA), which internal hard disk drive 414 can also beconfigured for external use in a suitable chassis (not shown), amagnetic floppy disk drive (FDD) 416, (e.g., to read from or write to aremovable diskette 418) and an optical disk drive 420, (e.g., reading aCD-ROM disk 422 or, to read from or write to other high capacity opticalmedia such as the DVD). The hard disk drive 414, magnetic disk drive 416and optical disk drive 420 can be connected to the system bus 408 by ahard disk drive interface 424, a magnetic disk drive interface 426 andan optical drive interface 428, respectively. The interface 424 forexternal drive implementations comprises at least one or both ofUniversal Serial Bus (USB) and Institute of Electrical and ElectronicsEngineers (IEEE) 1394 interface technologies. Other external driveconnection technologies are within contemplation of the embodimentsdescribed herein.

The drives and their associated computer-readable storage media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer 402, the drives and storagemedia accommodate the storage of any data in a suitable digital format.Although the description of computer-readable storage media above refersto a hard disk drive (HDD), a removable magnetic diskette, and aremovable optical media such as a CD or DVD, it should be appreciated bythose skilled in the art that other types of storage media which arereadable by a computer, such as zip drives, magnetic cassettes, flashmemory cards, cartridges, and the like, can also be used in the exampleoperating environment, and further, that any such storage media cancontain computer-executable instructions for performing the methodsdescribed herein.

A number of program modules can be stored in the drives and RAM 412,comprising an operating system 430, one or more application programs432, other program modules 434 and program data 436. All or portions ofthe operating system, applications, modules, and/or data can also becached in the RAM 412. The systems and methods described herein can beimplemented utilizing various commercially available operating systemsor combinations of operating systems.

A user can enter commands and information into the computer 402 throughone or more wired/wireless input devices, e.g., a keyboard 438 and apointing device, such as a mouse 440. Other input devices (not shown)can comprise a microphone, an infrared (IR) remote control, a joystick,a game pad, a stylus pen, touch screen or the like. These and otherinput devices are often connected to the processing unit 404 through aninput device interface 442 that can be coupled to the system bus 408,but can be connected by other interfaces, such as a parallel port, anIEEE 1394 serial port, a game port, a universal serial bus (USB) port,an IR interface, etc.

A monitor 444 or other type of display device can be also connected tothe system bus 408 via an interface, such as a video adapter 446. Itwill also be appreciated that in alternative embodiments, a monitor 444can also be any display device (e.g., another computer having a display,a smart phone, a tablet computer, etc.) for receiving displayinformation associated with computer 402 via any communication means,including via the Internet and cloud-based networks. In addition to themonitor 444, a computer typically comprises other peripheral outputdevices (not shown), such as speakers, printers, etc.

The computer 402 can operate in a networked environment using logicalconnections via wired and/or wireless communications to one or moreremote computers, such as a remote computer(s) 448. The remotecomputer(s) 448 can be a workstation, a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentappliance, a peer device or other common network node, and typicallycomprises many or all of the elements described relative to the computer402, although, for purposes of brevity, only a memory/storage device 450is illustrated. The logical connections depicted comprise wired/wirelessconnectivity to a local area network (LAN) 452 and/or larger networks,e.g., a wide area network (WAN) 454. Such LAN and WAN networkingenvironments are commonplace in offices and companies, and facilitateenterprise-wide computer networks, such as intranets, all of which canconnect to a global communications network, e.g., the Internet.

When used in a LAN networking environment, the computer 402 can beconnected to the local network 452 through a wired and/or wirelesscommunication network interface or adapter 456. The adapter 456 canfacilitate wired or wireless communication to the LAN 452, which canalso comprise a wireless AP disposed thereon for communicating with thewireless adapter 456.

When used in a WAN networking environment, the computer 402 can comprisea modem 458 or can be connected to a communications server on the WAN454 or has other means for establishing communications over the WAN 454,such as by way of the Internet. The modem 458, which can be internal orexternal and a wired or wireless device, can be connected to the systembus 408 via the input device interface 442. In a networked environment,program modules depicted relative to the computer 402 or portionsthereof, can be stored in the remote memory/storage device 450. It willbe appreciated that the network connections shown are example and othermeans of establishing a communications link between the computers can beused.

The computer 402 can be operable to communicate with any wirelessdevices or entities operatively disposed in wireless communication,e.g., a printer, scanner, desktop and/or portable computer, portabledata assistant, communications satellite, any piece of equipment orlocation associated with a wirelessly detectable tag (e.g., a kiosk,news stand, restroom), and telephone. This can comprise WirelessFidelity (Wi-Fi) and BLUETOOTH® wireless technologies. Thus, thecommunication can be a predefined structure as with a conventionalnetwork or simply an ad hoc communication between at least two devices.

Wi-Fi can allow connection to the Internet from a couch at home, a bedin a hotel room or a conference room at work, without wires. Wi-Fi is awireless technology similar to that used in a cell phone that enablessuch devices, e.g., computers, to send and receive data indoors and out;anywhere within the range of a base station. Wi-Fi networks use radiotechnologies called IEEE 802.11 (a, b, g, n, ac, ag etc.) to providesecure, reliable, fast wireless connectivity. A Wi-Fi network can beused to connect computers to each other, to the Internet, and to wirednetworks (which can use IEEE 802.3 or Ethernet). Wi-Fi networks operatein the unlicensed 2.4 and 5 GHz radio bands for example or with productsthat contain both bands (dual band), so the networks can providereal-world performance similar to the basic 10BaseT wired Ethernetnetworks used in many offices.

Turning now to FIG. 5, an embodiment 500 of a mobile network platform510 is shown that is an example of network elements 150, 152, 154, 156,and/or virtual network elements 330, 332, 334, etc. In one or moreembodiments, the mobile network platform 510 can generate and receivesignals transmitted and received by base stations or access points suchas base station or access point 122. Generally, wireless networkplatform 510 can comprise components, e.g., nodes, gateways, interfaces,servers, or disparate platforms, that facilitate both packet-switched(PS) (e.g., internet protocol (IP), frame relay, asynchronous transfermode (ATM)) and circuit-switched (CS) traffic (e.g., voice and data), aswell as control generation for networked wireless telecommunication. Asa non-limiting example, wireless network platform 510 can be included intelecommunications carrier networks, and can be considered carrier-sidecomponents as discussed elsewhere herein. Mobile network platform 510comprises CS gateway node(s) 512 which can interface CS traffic receivedfrom legacy networks like telephony network(s) 540 (e.g., publicswitched telephone network (PSTN), or public land mobile network (PLMN))or a signaling system #7 (SS7) network 570. Circuit switched gatewaynode(s) 512 can authorize and authenticate traffic (e.g., voice) arisingfrom such networks. Additionally, CS gateway node(s) 512 can accessmobility, or roaming, data generated through SS7 network 570; forinstance, mobility data stored in a visited location register (VLR),which can reside in memory 530. Moreover, CS gateway node(s) 512interfaces CS-based traffic and signaling and PS gateway node(s) 518. Asan example, in a 3GPP UMTS network, CS gateway node(s) 512 can berealized at least in part in gateway GPRS support node(s) (GGSN). Itshould be appreciated that functionality and specific operation of CSgateway node(s) 512, PS gateway node(s) 518, and serving node(s) 516, isprovided and dictated by radio technology(ies) utilized by mobilenetwork platform 510 for telecommunication.

In addition to receiving and processing CS-switched traffic andsignaling, PS gateway node(s) 518 can authorize and authenticatePS-based data sessions with served mobile devices. Data sessions cancomprise traffic, or content(s), exchanged with networks external to thewireless network platform 510, like wide area network(s) (WANs) 550,enterprise network(s) 570, and service network(s) 580, which can beembodied in local area network(s) (LANs), can also be interfaced withmobile network platform 510 through PS gateway node(s) 518. It is to benoted that WANs 550 and enterprise network(s) 560 can embody, at leastin part, a service network(s) like IP multimedia subsystem (IMS). Basedon radio technology layer(s) available in technology resource(s) 517,packet-switched gateway node(s) 518 can generate packet data protocolcontexts when a data session is established; other data structures thatfacilitate routing of packetized data also can be generated. To thatend, in an aspect, PS gateway node(s) 518 can comprise a tunnelinterface (e.g., tunnel termination gateway (TTG) in 3GPP UMTSnetwork(s) (not shown)) which can facilitate packetized communicationwith disparate wireless network(s), such as Wi-Fi networks.

In embodiment 500, wireless network platform 510 also comprises servingnode(s) 516 that, based upon available radio technology layer(s) withintechnology resource(s) 517, convey the various packetized flows of datastreams received through PS gateway node(s) 518. It is to be noted thatfor technology resource(s) that rely primarily on CS communication,server node(s) can deliver traffic without reliance on PS gatewaynode(s) 518; for example, server node(s) can embody at least in part amobile switching center. As an example, in a 3GPP UMTS network, servingnode(s) 516 can be embodied in serving GPRS support node(s) (SGSN).

For radio technologies that exploit packetized communication, server(s)514 in wireless network platform 510 can execute numerous applicationsthat can generate multiple disparate packetized data streams or flows,and manage (e.g., schedule, queue, format . . . ) such flows. Suchapplication(s) can comprise add-on features to standard services (forexample, provisioning, billing, customer support . . . ) provided bywireless network platform 510. Data streams (e.g., content(s) that arepart of a voice call or data session) can be conveyed to PS gatewaynode(s) 518 for authorization/authentication and initiation of a datasession, and to serving node(s) 516 for communication thereafter. Inaddition to application server, server(s) 514 can comprise utilityserver(s), a utility server can comprise a provisioning server, anoperations and maintenance server, a security server that can implementat least in part a certificate authority and firewalls as well as othersecurity mechanisms, and the like. In an aspect, security server(s)secure communication served through wireless network platform 510 toensure network's operation and data integrity in addition toauthorization and authentication procedures that CS gateway node(s) 512and PS gateway node(s) 518 can enact. Moreover, provisioning server(s)can provision services from external network(s) like networks operatedby a disparate service provider; for instance, WAN 550 or GlobalPositioning System (GPS) network(s) (not shown). Provisioning server(s)can also provision coverage through networks associated to wirelessnetwork platform 510 (e.g., deployed and operated by the same serviceprovider), such as the distributed antennas networks shown in FIG. 1(s)that enhance wireless service coverage by providing more networkcoverage.

It is to be noted that server(s) 514 can comprise one or more processorsconfigured to confer at least in part the functionality of macrowireless network platform 510. To that end, the one or more processorcan execute code instructions stored in memory 530, for example. It isshould be appreciated that server(s) 514 can comprise a content manager,which operates in substantially the same manner as describedhereinbefore.

In example embodiment 500, memory 530 can store information related tooperation of wireless network platform 510. Other operationalinformation can comprise provisioning information of mobile devicesserved through wireless platform network 510, subscriber databases;application intelligence, pricing schemes, e.g., promotional rates,flat-rate programs, couponing campaigns; technical specification(s)consistent with telecommunication protocols for operation of disparateradio, or wireless, technology layers; and so forth. Memory 530 can alsostore information from at least one of telephony network(s) 540, WAN550, enterprise network(s) 570, or SS7 network 560. In an aspect, memory530 can be, for example, accessed as part of a data store component oras a remotely connected memory store.

In order to provide a context for the various aspects of the disclosedsubject matter, FIG. 5, and the following discussion, are intended toprovide a brief, general description of a suitable environment in whichthe various aspects of the disclosed subject matter can be implemented.While the subject matter has been described above in the general contextof computer-executable instructions of a computer program that runs on acomputer and/or computers, those skilled in the art will recognize thatthe disclosed subject matter also can be implemented in combination withother program modules. Generally, program modules comprise routines,programs, components, data structures, etc. that perform particulartasks and/or implement particular abstract data types.

Turning now to FIG. 6, an illustrative embodiment of a communicationdevice 600 is shown. The communication device 600 can serve as anillustrative embodiment of devices such as data terminals 114, mobiledevices 124, vehicle 126, display devices 144 or other client devicesfor communication via either communications network 125.

The communication device 600 can comprise a wireline and/or wirelesstransceiver 602 (herein transceiver 602), a user interface (UI) 604, apower supply 614, a location receiver 616, a motion sensor 618, anorientation sensor 620, and a controller 606 for managing operationsthereof. The transceiver 602 can support short-range or long-rangewireless access technologies such as Bluetooth®, ZigBee®, WiFi, DECT, orcellular communication technologies, just to mention a few (Bluetooth®and ZigBee® are trademarks registered by the Bluetooth® Special InterestGroup and the ZigBee® Alliance, respectively). Cellular technologies caninclude, for example, CDMA-1X, UMTS/HSDPA, GSM/GPRS, TDMA/EDGE, EV/DO,WiMAX, SDR, LTE, as well as other next generation wireless communicationtechnologies as they arise. The transceiver 602 can also be adapted tosupport circuit-switched wireline access technologies (such as PSTN),packet-switched wireline access technologies (such as TCP/IP, VoIP,etc.), and combinations thereof.

The UI 604 can include a depressible or touch-sensitive keypad 608 witha navigation mechanism such as a roller ball, a joystick, a mouse, or anavigation disk for manipulating operations of the communication device600. The keypad 608 can be an integral part of a housing assembly of thecommunication device 600 or an independent device operably coupledthereto by a tethered wireline interface (such as a USB cable) or awireless interface supporting for example Bluetooth®. The keypad 608 canrepresent a numeric keypad commonly used by phones, and/or a QWERTYkeypad with alphanumeric keys. The UI 604 can further include a display610 such as monochrome or color LCD (Liquid Crystal Display), OLED(Organic Light Emitting Diode) or other suitable display technology forconveying images to an end user of the communication device 600. In anembodiment where the display 610 is touch-sensitive, a portion or all ofthe keypad 608 can be presented by way of the display 610 withnavigation features.

The display 610 can use touch screen technology to also serve as a userinterface for detecting user input. As a touch screen display, thecommunication device 600 can be adapted to present a user interfacehaving graphical user interface (GUI) elements that can be selected by auser with a touch of a finger. The touch screen display 610 can beequipped with capacitive, resistive or other forms of sensing technologyto detect how much surface area of a user's finger has been placed on aportion of the touch screen display. This sensing information can beused to control the manipulation of the GUI elements or other functionsof the user interface. The display 610 can be an integral part of thehousing assembly of the communication device 600 or an independentdevice communicatively coupled thereto by a tethered wireline interface(such as a cable) or a wireless interface.

The UI 604 can also include an audio system 612 that utilizes audiotechnology for conveying low volume audio (such as audio heard inproximity of a human ear) and high volume audio (such as speakerphonefor hands free operation). The audio system 612 can further include amicrophone for receiving audible signals of an end user. The audiosystem 612 can also be used for voice recognition applications. The UI604 can further include an image sensor 613 such as a charged coupleddevice (CCD) camera for capturing still or moving images.

The power supply 614 can utilize common power management technologiessuch as replaceable and rechargeable batteries, supply regulationtechnologies, and/or charging system technologies for supplying energyto the components of the communication device 600 to facilitatelong-range or short-range portable communications. Alternatively, or incombination, the charging system can utilize external power sources suchas DC power supplied over a physical interface such as a USB port orother suitable tethering technologies.

The location receiver 616 can utilize location technology such as aglobal positioning system (GPS) receiver capable of assisted GPS foridentifying a location of the communication device 600 based on signalsgenerated by a constellation of GPS satellites, which can be used forfacilitating location services such as navigation. The motion sensor 618can utilize motion sensing technology such as an accelerometer, agyroscope, or other suitable motion sensing technology to detect motionof the communication device 600 in three-dimensional space. Theorientation sensor 620 can utilize orientation sensing technology suchas a magnetometer to detect the orientation of the communication device600 (north, south, west, and east, as well as combined orientations indegrees, minutes, or other suitable orientation metrics).

The communication device 600 can use the transceiver 602 to alsodetermine a proximity to a cellular, WiFi, Bluetooth®, or other wirelessaccess points by sensing techniques such as utilizing a received signalstrength indicator (RSSI) and/or signal time of arrival (TOA) or time offlight (TOF) measurements. The controller 606 can utilize computingtechnologies such as a microprocessor, a digital signal processor (DSP),programmable gate arrays, application specific integrated circuits,and/or a video processor with associated storage memory such as Flash,ROM, RAM, SRAM, DRAM or other storage technologies for executingcomputer instructions, controlling, and processing data supplied by theaforementioned components of the communication device 600.

Other components not shown in FIG. 6 can be used in one or moreembodiments of the subject disclosure. For instance, the communicationdevice 600 can include a slot for adding or removing an identity modulesuch as a Subscriber Identity Module (SIM) card or Universal IntegratedCircuit Card (UICC). SIM or UICC cards can be used for identifyingsubscriber services, executing programs, storing subscriber data, and soon.

The terms “first,” “second,” “third,” and so forth, as used in theclaims, unless otherwise clear by context, is for clarity only anddoesn't otherwise indicate or imply any order in time. For instance, “afirst determination,” “a second determination,” and “a thirddetermination,” does not indicate or imply that the first determinationis to be made before the second determination, or vice versa, etc.

In the subject specification, terms such as “store,” “storage,” “datastore,” data storage,” “database,” and substantially any otherinformation storage component relevant to operation and functionality ofa component, refer to “memory components,” or entities embodied in a“memory” or components comprising the memory. It will be appreciatedthat the memory components described herein can be either volatilememory or nonvolatile memory, or can comprise both volatile andnonvolatile memory, by way of illustration, and not limitation, volatilememory, non-volatile memory, disk storage, and memory storage. Further,nonvolatile memory can be included in read only memory (ROM),programmable ROM (PROM), electrically programmable ROM (EPROM),electrically erasable ROM (EEPROM), or flash memory. Volatile memory cancomprise random access memory (RAM), which acts as external cachememory. By way of illustration and not limitation, RAM is available inmany forms such as synchronous RAM (SRAM), dynamic RAM (DRAM),synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhancedSDRAM (ESDRAIVI), Synchlink DRAM (SLDRAM), and direct Rambus RAM(DRRAM). Additionally, the disclosed memory components of systems ormethods herein are intended to comprise, without being limited tocomprising, these and any other suitable types of memory.

Moreover, it will be noted that the disclosed subject matter can bepracticed with other computer system configurations, comprisingsingle-processor or multiprocessor computer systems, mini-computingdevices, mainframe computers, as well as personal computers, hand-heldcomputing devices (e.g., PDA, phone, smartphone, watch, tabletcomputers, netbook computers, etc.), microprocessor-based orprogrammable consumer or industrial electronics, and the like. Theillustrated aspects can also be practiced in distributed computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network; however, some if not allaspects of the subject disclosure can be practiced on stand-alonecomputers. In a distributed computing environment, program modules canbe located in both local and remote memory storage devices.

Some of the embodiments described herein can also employ artificialintelligence (AI) to facilitate automating one or more featuresdescribed herein. The embodiments (e.g., in connection withautomatically selecting bandwidth prediction source(s)) can employvarious AI-based schemes for carrying out various embodiments thereof.Moreover, a classifier can be employed to determine a ranking orpriority of certain items (e.g., a ranking or priority of predictionsource(s) and/or a ranking or priority of prediction type(s)). Aclassifier is a function that maps an input attribute vector, x=(x1, x2,x3, x4, . . . , xn), to a confidence that the input belongs to a class,that is, f(x)=confidence (class). Such classification can employ aprobabilistic and/or statistical-based analysis (e.g., factoring intothe analysis utilities and costs) to prognose or infer an action that auser desires to be automatically performed. A support vector machine(SVM) is an example of a classifier that can be employed. The SVMoperates by finding a hypersurface in the space of possible inputs,which the hypersurface attempts to split the triggering criteria fromthe non-triggering events. Intuitively, this makes the classificationcorrect for testing data that is near, but not identical to trainingdata. Other directed and undirected model classification approachescomprise, e.g., naïve Bayes, Bayesian networks, decision trees, neuralnetworks, fuzzy logic models, and probabilistic classification modelsproviding different patterns of independence can be employed.Classification as used herein also is inclusive of statisticalregression that is utilized to develop models of priority.

As will be readily appreciated, one or more of the embodiments canemploy classifiers that are explicitly trained (e.g., via a generictraining data) as well as implicitly trained (e.g., via observing UEbehavior, operator preferences, historical information, receivingextrinsic information). For example, SVMs can be configured via alearning or training phase within a classifier constructor and featureselection module. Thus, the classifier(s) can be used to automaticallylearn and perform a number of functions, including but not limited todetermining according to predetermined criteria which of the predictionsource(s) and/or a which of the prediction type(s) to utilize, etc.

As used in some contexts in this application, in some embodiments, theterms “component,” “system” and the like are intended to refer to, orcomprise, a computer-related entity or an entity related to anoperational apparatus with one or more specific functionalities, whereinthe entity can be either hardware, a combination of hardware andsoftware, software, or software in execution. As an example, a componentmay be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution,computer-executable instructions, a program, and/or a computer. By wayof illustration and not limitation, both an application running on aserver and the server can be a component. One or more components mayreside within a process and/or thread of execution and a component maybe localized on one computer and/or distributed between two or morecomputers. In addition, these components can execute from variouscomputer readable media having various data structures stored thereon.The components may communicate via local and/or remote processes such asin accordance with a signal having one or more data packets (e.g., datafrom one component interacting with another component in a local system,distributed system, and/or across a network such as the Internet withother systems via the signal). As another example, a component can be anapparatus with specific functionality provided by mechanical partsoperated by electric or electronic circuitry, which is operated by asoftware or firmware application executed by a processor, wherein theprocessor can be internal or external to the apparatus and executes atleast a part of the software or firmware application. As yet anotherexample, a component can be an apparatus that provides specificfunctionality through electronic components without mechanical parts,the electronic components can comprise a processor therein to executesoftware or firmware that confers at least in part the functionality ofthe electronic components. While various components have beenillustrated as separate components, it will be appreciated that multiplecomponents can be implemented as a single component, or a singlecomponent can be implemented as multiple components, without departingfrom example embodiments.

Further, the various embodiments can be implemented as a method,apparatus or article of manufacture using standard programming and/orengineering techniques to produce software, firmware, hardware or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer program accessible from anycomputer-readable device or computer-readable storage/communicationsmedia. For example, computer readable storage media can include, but arenot limited to, magnetic storage devices (e.g., hard disk, floppy disk,magnetic strips), optical disks (e.g., compact disk (CD), digitalversatile disk (DVD)), smart cards, and flash memory devices (e.g.,card, stick, key drive). Of course, those skilled in the art willrecognize many modifications can be made to this configuration withoutdeparting from the scope or spirit of the various embodiments.

In addition, the words “example” and “exemplary” are used herein to meanserving as an instance or illustration. Any embodiment or designdescribed herein as “example” or “exemplary” is not necessarily to beconstrued as preferred or advantageous over other embodiments ordesigns. Rather, use of the word example or exemplary is intended topresent concepts in a concrete fashion. As used in this application, theterm “or” is intended to mean an inclusive “or” rather than an exclusive“or”. That is, unless specified otherwise or clear from context, “Xemploys A or B” is intended to mean any of the natural inclusivepermutations. That is, if X employs A; X employs B; or X employs both Aand B, then “X employs A or B” is satisfied under any of the foregoinginstances. In addition, the articles “a” and “an” as used in thisapplication and the appended claims should generally be construed tomean “one or more” unless specified otherwise or clear from context tobe directed to a singular form.

Moreover, terms such as “user equipment,” “mobile station,” “mobile,”subscriber station,” “access terminal,” “terminal,” “handset,” “mobiledevice” (and/or terms representing similar terminology) can refer to awireless device utilized by a subscriber or user of a wirelesscommunication service to receive or convey data, control, voice, video,sound, gaming or substantially any data-stream or signaling-stream. Theforegoing terms are utilized interchangeably herein and with referenceto the related drawings.

Furthermore, the terms “user,” “subscriber,” “customer,” “consumer” andthe like are employed interchangeably throughout, unless contextwarrants particular distinctions among the terms. It should beappreciated that such terms can refer to human entities or automatedcomponents supported through artificial intelligence (e.g., a capacityto make inference based, at least, on complex mathematical formalisms),which can provide simulated vision, sound recognition and so forth.

As employed herein, the term “processor” can refer to substantially anycomputing processing unit or device comprising, but not limited tocomprising, single-core processors; single-processors with softwaremultithread execution capability; multi-core processors; multi-coreprocessors with software multithread execution capability; multi-coreprocessors with hardware multithread technology; parallel platforms; andparallel platforms with distributed shared memory. Additionally, aprocessor can refer to an integrated circuit, an application specificintegrated circuit (ASIC), a digital signal processor (DSP), a fieldprogrammable gate array (FPGA), a programmable logic controller (PLC), acomplex programmable logic device (CPLD), a discrete gate or transistorlogic, discrete hardware components or any combination thereof designedto perform the functions described herein. Processors can exploitnano-scale architectures such as, but not limited to, molecular andquantum-dot based transistors, switches and gates, in order to optimizespace usage or enhance performance of user equipment. A processor canalso be implemented as a combination of computing processing units.

As used herein, terms such as “data storage,” data storage,” “database,”and substantially any other information storage component relevant tooperation and functionality of a component, refer to “memorycomponents,” or entities embodied in a “memory” or components comprisingthe memory. It will be appreciated that the memory components orcomputer-readable storage media, described herein can be either volatilememory or nonvolatile memory or can include both volatile andnonvolatile memory.

What has been described above includes mere examples of variousembodiments. It is, of course, not possible to describe everyconceivable combination of components or methodologies for purposes ofdescribing these examples, but one of ordinary skill in the art canrecognize that many further combinations and permutations of the presentembodiments are possible. Accordingly, the embodiments disclosed and/orclaimed herein are intended to embrace all such alterations,modifications and variations that fall within the spirit and scope ofthe appended claims. Furthermore, to the extent that the term “includes”is used in either the detailed description or the claims, such term isintended to be inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

As may also be used herein, the term(s) “operably coupled to”, “coupledto”, and/or “coupling” includes direct coupling between items and/orindirect coupling between items via one or more intervening items. Suchitems and intervening items include, but are not limited to, junctions,communication paths, components, circuit elements, circuits, functionalblocks, and/or devices. As an example of indirect coupling, a signalconveyed from a first item to a second item may be modified by one ormore intervening items by modifying the form, nature or format ofinformation in a signal, while one or more elements of the informationin the signal are nevertheless conveyed in a manner than can berecognized by the second item. In a further example of indirectcoupling, an action in a first item can cause a reaction on the seconditem, as a result of actions and/or reactions in one or more interveningitems.

Although specific embodiments have been illustrated and describedherein, it should be appreciated that any arrangement which achieves thesame or similar purpose may be substituted for the embodiments describedor shown by the subject disclosure. The subject disclosure is intendedto cover any and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, can be used in the subject disclosure.For instance, one or more features from one or more embodiments can becombined with one or more features of one or more other embodiments. Inone or more embodiments, features that are positively recited can alsobe negatively recited and excluded from the embodiment with or withoutreplacement by another structural and/or functional feature. The stepsor functions described with respect to the embodiments of the subjectdisclosure can be performed in any order. The steps or functionsdescribed with respect to the embodiments of the subject disclosure canbe performed alone or in combination with other steps or functions ofthe subject disclosure, as well as from other embodiments or from othersteps that have not been described in the subject disclosure. Further,more than or less than all of the features described with respect to anembodiment can also be utilized.

What is claimed is:
 1. A device comprising: a processing systemincluding a processor; and a memory that stores executable instructionsthat, when executed by the processing system, facilitate performance ofoperations, the operations comprising: selecting as a source of a futurebandwidth prediction either a first source of information or a secondsource of information, the first source of information having supplied afirst bandwidth prediction that is based upon historical bandwidth datathat had been provided from a plurality of devices, the second source ofinformation having supplied a second bandwidth prediction that is basedupon network measurements other than the historical bandwidth data thathad been provided from the plurality of devices, the selecting beingbased upon a comparison of each of the first bandwidth prediction andthe second bandwidth prediction to an actually obtained bandwidth of thedevice, and the comparison comprising a determination of whether thefirst bandwidth prediction or the second bandwidth prediction is closerto the actually obtained bandwidth of the device; and selecting a videoquality to request for a future video segment, the video quality beingselected based upon the future bandwidth prediction that is obtainedfrom the source of the future bandwidth prediction.
 2. The device ofclaim 1, wherein the video quality is selected from a group of videoqualities, each quality of the group of video qualities having adistinct bitrate.
 3. The device of claim 1, wherein the first source ofinformation comprises a first server that communicates with the devicevia a network and wherein the second source of information comprises asecond server that communicates with the device via the network.
 4. Thedevice of claim 1, wherein the first source of information comprises aserver that communicates with the device via a network and wherein thesecond source of information comprises the server that communicates withthe device via the network.
 5. The device of claim 1, wherein the deviceis a mobile communication device and wherein the first bandwidthprediction, the second bandwidth prediction, and the future bandwidthprediction are predictions of bandwidth available to the mobilecommunication device via a mobile communication network.
 6. The deviceof claim 5, wherein the mobile communication device comprises a cellphone, a smartphone, a tablet, a laptop computer, or any combinationthereof.
 7. The device of claim 5, wherein the first bandwidthprediction is based upon the historical bandwidth data that had beenprovided to the mobile communication network by the plurality ofdevices.
 8. The device of claim 1, wherein the plurality of devicesincludes the device.
 9. The device of claim 1, wherein the device isconfigured to communicate with a network, and wherein the secondbandwidth prediction is based upon the network measurements that areassociated with the network.
 10. The device of claim 1, wherein thenetwork measurements are carried out by one or more elements of anetwork with which the device is configured to communicate.
 11. Thedevice of claim 11, wherein the network measurements comprise one ormore signal-to-noise ratios, one or more signal qualities, one or moreradio resource utilizations, or any combination thereof.
 12. The deviceof claim 1, wherein: the first source of information is selected as thesource of the future bandwidth prediction in a first case that the firstbandwidth prediction is closer to the actually obtained bandwidth of thedevice than the second bandwidth prediction; and the second source ofinformation is selected as the source of the future bandwidth predictionin a second case that the second bandwidth prediction is closer to theactually obtained bandwidth of the device than the first bandwidthprediction.
 13. The device of claim 1, wherein the actually obtainedbandwidth of the device is determined by the device.
 14. The device ofclaim 13, wherein the actually obtained bandwidth of the device isdetermined by the device from information that is local to the deviceand wherein each of the first source of information and the secondsource of information are remote relative to the device.
 15. Amachine-readable storage medium comprising executable instructions that,when executed by a processing system of a video player including aprocessor, facilitate performance of operations, the operationscomprising: selecting, by the processing system, as a source of a futurebandwidth prediction either a first source of information or a secondsource of information, the first source of information having supplied afirst bandwidth prediction that is based upon historical bandwidth datathat had been provided to a server on a network from a plurality ofdevices, the second source of information having supplied a secondbandwidth prediction that is based upon network measurements other thanthe historical bandwidth data that had been provided from the pluralityof devices, the selecting being based upon a comparison of each of thefirst bandwidth prediction and the second bandwidth prediction to anactually obtained bandwidth of the processing system during receipt of afirst video segment by the processing system, and the comparisoncomprising a determination of whether the first bandwidth prediction orthe second bandwidth prediction is closer to the actually obtainedbandwidth of the processing system; selecting a bitrate corresponding toa video quality to request for a future video segment, the bitrate beingselected based upon the future bandwidth prediction that is obtainedfrom the source of the future bandwidth prediction; and requesting, fromthe processing system, the future video segment having the bitrate thathad been selected.
 16. The machine-readable storage medium of claim 15,wherein the bitrate is selected from a group of bitrates.
 17. Themachine-readable storage medium of claim 16, wherein the networkmeasurements comprise one or more signal-to-noise ratios, one or moresignal qualities, one or more radio resource utilizations, or anycombination thereof.
 18. A method comprising: selecting, by a videoplayer comprising a processing system with a processor, as a source of afuture bandwidth prediction either a first source of information or asecond source of information, the first source of information havingsupplied a first bandwidth prediction that is based upon historicalbandwidth data that had been provided from a plurality of devices, thesecond source of information having supplied a second bandwidthprediction that is based upon network measurements other than thehistorical bandwidth data that had been provided from the plurality ofdevices, the selecting being based upon a comparison of each of thefirst bandwidth prediction and the second bandwidth prediction to anactually obtained bandwidth of the video player while receiving a firstportion of video, and the comparison comprising a determination ofwhether the first bandwidth prediction or the second bandwidthprediction is closer to the actually obtained bandwidth of the videoplayer while receiving the first portion of video; and selecting a videoquality to request for a second portion of video, the video qualitybeing selected based upon the future bandwidth prediction that isobtained from the source of the future bandwidth prediction; andrequesting, by the video player, from a video source, the second portionof the video having the video quality.
 19. The method of claim 18,wherein the video source comprises a video server on the network. 20.The method of claim 18, wherein the network measurements comprise one ormore signal-to-noise ratios, one or more signal qualities, one or moreradio resource utilizations, or any combination thereof.